def pintar_imagen():
ancho = 300
alto = 200
img = Image.open('imagentiempo1.png', "rb")
img.thumbnail((ancho, alto), Image.ANTIALIAS)
imagen1 = ImageTk.PhotoImage(img)
label1 = Label(ventana, image=imagen1, width=ancho,
height=alto).place(x=1, y=75)
img2 = Image.open('imagenfreq.png', "rb")
img2.thumbnail((ancho, alto), Image.ANTIALIAS)
imagen2 = ImageTk.PhotoImage(img2)
label2 = Label(ventana, image=imagen2, width=ancho,
height=alto).place(x=310, y=75)
img3 = Image.open('imagenbode.png', "rb")
img3.thumbnail((ancho, alto), Image.ANTIALIAS)
imagen3 = ImageTk.PhotoImage(img3)
label3 = Label(ventana, image=imagen3, width=ancho,
height=alto).place(x=620, y=75)
img4 = Image.open('filtrado.png', "rb")
img4.thumbnail((ancho, alto), Image.ANTIALIAS)
imagen4 = ImageTk.PhotoImage(img4)
label4 = Label(ventana, image=imagen4, width=ancho,
height=alto).place(x=620, y=280)
img5 = Image.open('filtradoFFT.png', "rb")
img5.thumbnail((ancho, alto), Image.ANTIALIAS)
imagen5 = ImageTk.PhotoImage(img5)
label5 = Label(ventana, image=imagen5, width=ancho,
height=alto).place(x=620, y=490)
Image.show()
def display_img(img: Image):
"""
Given an Image instance, display result
:param img: PIL.Image instance
:return: None
"""
img.show()
def openfile():
filename = askopenfilename(parent=root)
img = ImageTk.PhotoImage(Image.open(filename))
print(filename)
panel = Label(root, image = img)
panel.pack(side = "bottom", fill = "both", expand = "yes")
Image.show()
def Fourier():
filename = askopenfilename(parent=root)
img = Image.open(filename)
fft = np.log(np.abs(np.fft.fftshift(np.fft.fft(img))))
panel = Label(root, image = ImageTk.PhotoImage(fft))
panel.pack(side = "bottom", fill = "both", expand = "yes")
Image.show()
def find_white_squers(img: Image):
width, hight = img.size
split = img.split()[2]
pix = split.load()
ws = []
cv_img = np.array(img)
h, w = cv_img.shape[:2]
mask = np.zeros((h + 2, w + 2), np.uint8)
rect_width, rec_hight = int(width / 10), int(hight / 10)
size = lambda i, j: (max(i - rect_width, 0), max(j - rec_hight, 0),
min(rect_width + i, width), min(rec_hight + j, hight))
for i in range(rect_width, int(width - rect_width / 4),
int(rect_width / 4)):
for j in range(rec_hight, int(hight - rec_hight / 4),
int(rec_hight / 4)):
image_crop = split.crop(size(i, j))
stat = ImageStat.Stat(image_crop)
white_thresh = (np.array(stat.mean) + np.array(stat.stddev))
if (pix[i, j] > white_thresh):
#image_crop.show()
diff = (255, 255, int(white_thresh / 10))
_, _, _, rect = cv2.floodFill(cv_img,
mask=mask,
seedPoint=(i, j),
newVal=255,
loDiff=diff,
upDiff=diff,
flags=cv2.FLOODFILL_FIXED_RANGE)
ws.append((rect, white_thresh))
#cv2.imshow('subpixel5.png', cv_img)
# filter rects
ws = [((r[0][0], r[0][1], r[0][0] + r[0][2], r[0][1] + r[0][3]),
white_thresh) for r in ws if rec_hight > r[0][3] > (rec_hight / 7)
and (rect_width * 2) > r[0][2] > (rect_width / 1.7)]
for r1 in ws:
for r2 in ws:
if (r1[0] != r2[0]):
if (all(
abs(r1v - r2v) < (rect_width / 2)
for r1v, r2v in zip(r1[0], r2[0]))):
ws.remove(r2)
# if(len(ws) < 25):
# assert (False)
# return find_white_squers(img)
# print(ws)
# print(len(ws))
imgD = ImageDraw.Draw(img)
res = []
for w in ws:
res.append((w[0], img.crop(w[0]), w[1]))
imgD.rectangle(w[0], outline="red", fill="yellow")
img.show()
return res
class Image:
"""Represents the raw formt of an image that is loaded"""
def __init__(self, path):
self.path = path
self.form = path[-3:].capitalize()
self.pil = Image(path)
def __repr__(self):
return """
Path: {},
Height:{},
Width: {},
Color Mode: {},
Format: {}
""".format(self.path, self.pil.height, self.pil.width, self.pil.mode, self.form)
def __str__(self):
return """
\nPath: {},
\nHeight:{},
\nWidth: {},
\nColor Mode: {},
\nFormat: {}
""".format(self.path, self.pil.height, self.pil.width, self.pil.mode, self.form)
def plot(self):
self.pil.show()
def computeFourierTransforms(self, resultbmp):
self.fourierTransform.setImage(self.image.image)
fimg = Image()
fimg.set(
np.log(
np.abs(
self.fourierTransform.shift(
self.fourierTransform.forwardTransform()))**2))
fimg.show(resultbmp)
def Normal(File_Name):
password = input("password for that file: ")
encryptor = get_encrypt(password)
fileHandle = open_file(File_Name)
if (False == fileHandle):
FailedToOpenFile = input(
"Failed to find %s. Do you want to create a file with this name and password? (Yes/No): "
% (File_Name))
if (yesMatch.match(FailedToOpenFile)):
fileHandle = open_file(File_Name, force=True, encryptor=encryptor)
if (noMatch.match(FailedToOpenFile)):
exit(1)
DataBase = pickle.loads(encryptor.decrypt(fileHandle.read()))
fileHandle.close()
while True:
print("Do you want to")
print(
"1. View Database\n2. Add to the Database\n3. Delete items\n4. or exit the program"
)
choice = input(" type 1, 2, 3, or 4: ")
if int(choice) == 1:
for element in DataBase:
if (isinstance(element, str)):
print(element)
elif (isinstance(element, dict)):
Image.show(element["Image"])
elif int(choice) == 2:
TextOrImage = input(
'do you want to add text or an image? (Text/Image) ')
if (textMatch.match(TextOrImage)):
# do something with text
choice = input('Type first thing to add to database: ')
DataBase.append(choice)
elif (imageMatch.match(TextOrImage)):
# do something with an image
AddImage = input("what is the path to the Image file?")
im = Image.open(AddImage)
DataBase.append({"Image": im.tobytes(encoder_name='raw')})
else:
print("What?")
write_file(File_Name, DataBase, encryptor)
elif int(choice) == 3:
print(DataBase)
choice = input("\n\nWhich Item to delete? Enter name ")
DataBase.remove(choice)
write_file(File_Name, DataBase, encryptor)
elif int(choice) == 4:
exit(0)
def im_cov_cifar10(image):
reshaped_image = tf.cast(image, tf.float32)
height = IMAGE_SIZE
width = IMAGE_SIZE
resized_image = tf.image.resize_image_with_crop_or_pad(
reshaped_image, width, height)
float_image = tf.image.per_image_standardization(resized_image)
image = Image(float_image)
image.show()
def wait_qr_value(self, qr_img: Image) -> str:
if __file__.endswith(".ipynb"):
plt.axis("off")
plt.imshow(qr_img)
plt.show()
else:
SCALE = 4
qr_img.resize((qr_img.width * SCALE, qr_img.width * SCALE), Image.ANTIALIAS)
qr_img.show()
return input("Verify Code: ")
def decoupe(image, x, y, taille_capteur_long, taille_capteur_hauteur, xh, xb,
yd, yg):
#Cette méthode renvoie une image découpée à partir des coordonnées (x,y) ainsi que la taille du capteur
try:
test(image, x, y, taille_capteur_long, taille_capteur_hauteur)
test007(image, x, y, taille_capteur_long, taille_capteur_hauteur)
box = (x, y, x + taille_capteur_long, y + taille_capteur_hauteur)
coupe = image.crop(box)
Image.show(coupe)
return (coupe)
except MyException as e:
print(e)
exit()
def upload_image():
global img
filepath = filedialog.askopenfilename(initialdir="/",
title="Select A File",
filetype=(("jpeg", "*.jpg"),
("png", "*.png")))
img = Image.open(filepath)
label_width = label.winfo_width()
label_height = label.winfo_height()
maxsize = (label_width, label_height)
resized_original = img.resize(maxsize, Image.ANTIALIAS)
ph_original = ImageTk.PhotoImage(img)
label['image'] = ph_original
Image.show()
def show(self):
img = Image(self.video_id, self.timestamp).pil_image()
draw = PIL.ImageDraw.Draw(img)
draw.rectangle([(self.x, self.y), (self.x+self.width, self.y+self.height)])
if self.face_info:
parts = self.face_info.parts
for i in range(0,len(parts),2):
box = [(parts[i]-1, parts[i+1]-1),(parts[i]+1, parts[i+1]+1)]
draw.ellipse(box)
for i, j in [(0,1), (2,3), (1,2), (7,8)]:
x = (parts[2*i] + parts[2*j])/2
y = (parts[2*i+1] + parts[2*j+1])/2
box = [x-1, y-1, x+1, y+1]
draw.ellipse(box)
img.show()
del draw
def get_image(driver, div):
'''
下载并还原图片
:driver:webdriver
:div:图片的div
'''
pass
# 找到图片所在的div
background_images = driver.find_elements_by_css_selector(div)
location_list = []
print(len(background_images))
imageurl = ''
for background_image in background_images:
location = {}
# 在html里面解析出小图片的url地址，还有长高的数值
location['x'] = int(
re.findall(
"background-image: url\(\"(.*)\"\); background-position: (.*)px (.*)px;",
background_image.get_attribute('style'))[0][1])
print(location['x'])
location['y'] = int(
re.findall(
"background-image: url\(\"(.*)\"\); background-position: (.*)px (.*)px;",
background_image.get_attribute('style'))[0][2])
imageurl = re.findall(
"background-image: url\(\"(.*)\"\); background-position: (.*)px (.*)px;",
background_image.get_attribute('style'))[0][0]
location_list.append(location)
imageurl = imageurl.replace("webp", "jpg")
jpgfile = io.BytesIO(requests.get(imageurl).content)
# 重新合并图片
image = get_merge_image(jpgfile, location_list)
image.show()
return image
from PIL import Image
Image = Image.open('fibonacci_beard.jpg') # must be in same folder
width, height = Image.size
pixels = Image.load()
for i in range(width):
for j in range(height):
r, g, b = pixels[i, j]
r, g, b = int(r), int(g), int(b)
y = sum([r, g, b]) // 3
pixels[i, j] = (y, y, y)
Image.show()
def show():
image.show()
def takeScreenShot():
Image = ImageGrab.grab().convert('L')
Image.show()
return Image
def preview_pil(img: PIL.Image):
img.show()
input(f'bbox is {unpad_bbox(img)}')
def show_image(image: Image, opencv=False):
image.show() if not opencv else _show_image_cv('image', image)
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 3 10:49:58 2015
@author: Marina
"""
# This script is to change the color view of an image, in can change to black and white, tosepia, to red or militar green.
from PIL import Image
img = Image('street.JPG')
img.show()
from PIL import Image
qr = qrcode.QRCode(
version=1,
error_correction=qrcode.constants.ERROR_CORRECT_L,
box_size=10,
border=4,
)
qr.add_data('https://zwmusic.herokuapp.com')
qr.make(fit=True)
img = qr.make_image(fill_color="black", back_color="white")
print(img)
Image.show(img)
img.save('zwapp.png')
def show_img(image): Image.show(image)
im_p = copy.deepcopy(self.img)
height, width = im_p.shape[:2]
w = 0
for h in xrange(int(height)):
while int(width) >= w | self.spin1.value() != 0:
p_data = poisson(lam=self.spin1.value())
w+=int(p_data)
try: im_p[h,w] = [0,0,0]
except: pass
w = 0
cv2.imshow("poisson_noise",im_p)
def hanamegane(self):
self.face_know()
from PIL import Image
layer1 = Image.open(self.fname)
layer2 = Image.open('megane.png')
layer1 = layer1.convert('RGBA')
temp_c = Image.new('RGBA',layer1.size,(255,255,255,0))
for (x, y, w, h) in self.face:
resize_megane = layer2.resize((w,h))
temp_c.paste(resize_megane,(x,y),resize_megane)
result = Image.alpha_composite(layer1,temp_c)
result.show()
if __name__ == "__main__":
sys.stdout = codecs.getwriter("utf-8")(sys.stdout)
app = QApplication(sys.argv)
wi = Image()
wi.show()
app.exec_()
import numpy as np
import cv2
from PIL import Image
image = cv2.imread("001.jpg")
faceCascade = cv2.CascadeClassifier('tongue_cascade.xml')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.65, #该参数需要根据自己训练的模型进行调参
minNeighbors=3, #控制误检测，为3表明至少要3次重叠检测，才认为舌头存在
minSize=(30, 30), #寻找舌头的最小区域。
flags=cv2.IMREAD_GRAYSCALE)
cropBox = ()
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
box1 = (x, y, x + w, y + h) # 设置舌体矩形框区域
cv2.imwrite("rec001.jpg", image)
pil_image = Image.open("rec001.jpg") #使用PIL来编辑图片
image_2 = pil_image.crop(cropBox) # 图像裁剪--根据识别出的特征坐标剪切
Image.show(image2)
def display(self, image: Image):
image.show(command='feh')
value) # use decode_png or decode_jpeg decoder based on your files.
init_op = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init_op)
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
for i in range(1): #length of your filename list
image = my_img.eval() #here is your image Tensor :)
print(image.shape)
Image.show(Image.fromarray(np.asarray(image)))
coord.request_stop()
coord.join(threads)
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
# images and labels array as input
def convert_to(images, labels, name):
from PIL import Image as im
from PIL import ImageFilter as ift
i = im.open("winners.jpg")
im = i.filter(ift.BLUR)
im.show()
def display_image(image: Image):
image.show()
args=(
plateau,
(0, 255, 0),
(0, 0, 255),
(0.01, 0.01, 0.98),
1,
200000,
lock,
))
thread_3 = Thread(target=run_ant,
args=(
plateau,
(0, 0, 255),
(0, 0, 255),
(0.03, 0.02, 0.95),
0.4,
200000,
lock,
))
thread_1.start()
thread_2.start()
thread_3.start()
thread_1.join()
thread_2.join()
thread_3.join()
Image.show(plateau)
plateau.save("plateau.png", "png")
color_lut : opencv compatible color lookup table
"""
tobits = lambda x, o: np.array(list(np.binary_repr(x, 24)[o::-3]), np.uint8)
arr = np.arange(256)
r = np.concatenate([np.packbits(tobits(x, -3)) for x in arr])
g = np.concatenate([np.packbits(tobits(x, -2)) for x in arr])
b = np.concatenate([np.packbits(tobits(x, -1)) for x in arr])
return np.concatenate([[[b]], [[g]], [[r]]]).T
def labels2rgb(labels, lut):
"""
Convert a label image to an rgb image using a lookup table
:Parameters:
labels : an image of type np.uint8 2D array
lut : a lookup table of shape (256, 3) and type np.uint8
:Returns:
colorized_labels : a colorized label image
"""
return cv2.LUT(cv2.merge((labels, labels, labels)), lut)
if __name__ == '__main__':
#labels = np.arange(256).astype(np.uint8)[np.newaxis, :]
labels = np.loadtxt('./pics/result_texton_coins_texture3_20201215002820.txt',dtype=np.uint8)
lut = gen_lut()
print(labels.shape)
rgb = labels2rgb(labels, lut)
image = image.fromarray(rgb)
print(rgb.shape)
image.show()
def open_image(image: Image):
image.show()
def show_image(img: Image) -> None:
# show the PIL image
img.show()
